ssw-r offers an R interface for SSW, a fast implementation of the Smith-Waterman algorithm for sequence alignment using SIMD. ssw-r is currently built on the Python package ssw-py.
You can install ssw-r from CRAN once available:
install.packages("ssw")
Or try the development version on GitHub:
::install_github("nanxstats/ssw-r") remotes
A simple way to install the Python package ssw-py that ssw-r can
discover easily, is to run the helper function
ssw::install_ssw_py()
. By default, it installs ssw-py into
an virtual environment named r-ssw-py
.
::install_ssw_py() ssw
This follows the best practices suggested by the reticulate vignette Managing an R Package’s Python Dependencies. There are also recommendations in the vignette on how to manage multiple R packages with different Python dependencies.
library("ssw")
"ACGT" |> align("TTTTACGTCCCCC")
CIGAR start index 4: 4M
optimal_score: 8
sub-optimal_score: 0
target_begin: 4 target_end: 7
query_begin: 0
query_end: 3
Target: 4 ACGT 7
||||
Query: 0 ACGT 3
"ACGT" |> align("TTTTACTCCCCC", gap_open = 3)
CIGAR start index 4: 2M
optimal_score: 4
sub-optimal_score: 0
target_begin: 4 target_end: 5
query_begin: 0
query_end: 1
Target: 4 AC 5
||
Query: 0 AC 1
"ACTG" |> force_align("TTTTCTGCCCCCACG") |> formatter(print = TRUE)
TTTTCTGCCCCCACG
ACTG
For detailed usage, see the vignette.
ssw-r is built upon the work of two outstanding projects:
We extend our sincere gratitude to Mengyao Zhao for developing the original SSW library and to Nick Conway for maintaining the ssw-py package. Their work forms the foundation of ssw-r. While ssw-r does not directly incorporate code from these projects, it serves as an R interface to their functionality. We encourage users to visit the original repositories for more information about the underlying implementation and to consider citing these works in publications that use ssw-r.
Please note that the ssw-r project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.